AI models like deep learning and gradient boosting help solve many problems. They can find patterns in big data sets and make accurate predictions. But there's a big issue: they are hard to understand. Experts call them "blackbox models".
Blackbox models are so complex that no one can fully grasp how they work. They make decisions based on many factors all at once. This complexity makes it almost impossible to predict their behavior. If we use these models more in daily life, we need to know how they work.
As these models become more common, they start to play bigger roles in society. They can make important decisions, like who gets a loan or who gets medical help first. If we can't understand them, it can lead to distrust. People need to trust these models for them to be useful.
One idea to solve this is to make the models simpler. When models are simpler, people can understand them better. This can help build trust. But simpler models may not be as strong or accurate. So, it's a tricky balance.
Another idea is to create tools that explain the decisions of these models. These tools can show why a model made a certain choice. For example, if an AI decides not to give someone a loan, the tool can explain why. This helps people see if the decision was fair.
Researchers are working hard to solve this problem. They are developing ways to make blackbox models more transparent. They hope to create systems that everyone can understand. This can help people feel more comfortable with AI in their lives.
Understanding AI is key as it becomes a bigger part of our world. It's important for everyone to know how these models work and why they make the decisions they do. This can make AI a helpful and trusted tool in our society.